Industrial equipment or assets, generally, are engineered to perform particular tasks as part of a business process. For example, industrial assets can include, among other things and without limitation, manufacturing equipment on a production line, wind turbines that generate electricity on a wind farm, healthcare or imaging devices (e.g., X-ray or MRI systems) for use in patient care facilities, or drilling equipment for use in mining operations. The design and implementation of these assets often considers both the physics of the task at hand, as well as the environment in which such assets are configured to operate.
Low-level software and hardware-based controllers have long been used to drive industrial assets. However, the rise of inexpensive cloud computing, increasing sensor capabilities, and decreasing sensor costs, as well as the proliferation of mobile technologies have created opportunities for creating novel industrial assets with improved sensing technology that are capable of transmitting data that can then be transmitted to a network.
By transmitting locally acquired sensor and environment data to a computing infrastructure, this data may be processed and analyzed to measure and predict the behavior of the underlying assets. Predictive models can assist with determining the likelihood of particular outcomes based on sensor data received from the asset, past performance of the same or similar assets, predicted future performance of the same or similar assets, and the like.
However, predictive models are only useful in scenarios when they receive good input data. Models receiving faulty or incomplete data are likely to provide erroneous or incomplete results. Even worse, some predictive model authoring techniques use machine learning to “train” models that are subsequently used to predict outcomes. If the data used to train these models is incomplete or incorrect, the resulting predictive models are also likely to provide incorrect results, even if the data later provided as input is correct.
Recognizing these and other problems with the implementation of frameworks for authoring and utilizing predictive models, the inventors have developed methods and systems for reviewing, reconciling, reconstructing, estimating, and imputing data from received data records. It would therefore be desirable to provide a framework for authoring and/or executing predictive models that provides the capability to detect cases where data records are incomplete or inconsistent and estimate or impute data to correct the missing or inconsistent data.